{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mxnet import nd,gluon,autograd\n",
    "import matplotlib.pyplot as plt\n",
    "# %matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(xs,i,sample_size=30):\n",
    "    plotxs=xs[:sample_size,:]\n",
    "    plotxn=xs[:sample_size,1].asnumpy()\n",
    "    yhat=net(plotxs).asnumpy()\n",
    "    ax[i//3,i-3*(i//3)].plot(plotxn,yhat,'ro',label='True')\n",
    "\n",
    "    ys=nd.dot(plotxs,true_w)+true_b\n",
    "    ax[i//3,i-3*(i//3)].plot(plotxn,ys.asnumpy(),'y*',label='predict')\n",
    "    ax[i//3,i-3*(i//3)].grid(True)\n",
    "    if i==0:\n",
    "        ax[i//3,i-3*(i//3)].legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "true_w=nd.array([3,100])\n",
    "true_b=nd.array([1.3])\n",
    "def getDataSet():\n",
    "    xs=nd.random_normal(shape=(600,2))\n",
    "    ys=nd.dot(xs,true_w)+true_b\n",
    "    ys+=0.01*nd.random_normal(shape=ys.shape)\n",
    "    return xs,ys\n",
    "xs,ys=getDataSet()\n",
    "dataset=gluon.data.ArrayDataset(xs,ys)\n",
    "data_iter=gluon.data.DataLoader(dataset,batch_size=30,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net=gluon.nn.Sequential()\n",
    "net.add(gluon.nn.Dense(1))\n",
    "net.initialize()\n",
    "dense=net[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "square_loss=gluon.loss.L2Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainer=gluon.Trainer(dense.params,'sgd',{'learning_rate':0.1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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NMQm2LD0cWculfKU3utasGdpb+tZbKS/peBjaH/eGIjHvJxYGHPSdGdI8GCkP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\nncnaTAo1dwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c8eb0bed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs=10\n",
    "i=0\n",
    "fig,ax=plt.subplots(3,3,figsize=(10, 10)) \n",
    "for e in range(epochs):\n",
    "    for x,y in data_iter:          #计算出所有数据的损失\n",
    "        with autograd.record():\n",
    "            yhat=net(x)\n",
    "            loss=square_loss(yhat,y)\n",
    "        loss.backward()\n",
    "        trainer.step(10)         \n",
    "        if i<9:\n",
    "            plot(x,i)\n",
    "        i+=1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[   3.  100.]\n",
       "<NDArray 2 @cpu(0)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[[  3.00110841  99.9997406 ]]\n",
       "<NDArray 1x2 @cpu(0)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense.weight.data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
